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Click Through Rates in Google SERPs for Different Types of Queries

This entry was written by one of our members and submitted to our YouMoz section.The author's views below are entirely his or her own and may not reflect the views of Moz.

Bluerank specialists have been analyzing web search to provide the best service to our clients. Web search is based on users, and to achieve the best results we have to understand users’ actions.

I was interested in click through rates in Google search engine result pages. It is obvious that the position in search engine result pages is vital. However, it is just as important that there are three main types of keywords, and these keywords might have different click through rates. To find out if that thesis is true, I conducted a study.

Brief methodology

I built a database of 14,507 queries, their CTRs, and average positions. Data was gathered from Google Webmaster Tools from different types of websites (including e-commerce, institution website, company website and classifieds websites). The collected database includes various queries, which gives some broader perspective.

Each query was analyzed and marked as brand if it contained the domain name. Queries were checked and marked as “Product queries” if they contained product names. If the query was neither brand nor product, it was marked as “General.”

You can find expanded methodology at the bottom of the post.

Key findings

The main conclusion of this analysis is that, depending on the type of queries a user chooses, their actions in web search differ. While preparing the plan for long-term SEO projects, we can assume priorities for different types of keywords. The last conclusion concerns the issue of long tail phrases that can’t be ignored. Websites have to be prepared properly so that they are ready to serve good landing pages for long tail queries.

Queries prioritization

It is obvious that reaching high positions is important. But if you want to prioritize queries and plan to reach the highest positions first with most important queries, it would be best to start with product queries, then with general queries, and after that with brand queries.

Average CTR of all queries

As you can see on the graph above, top1 is most popular (52%) for all the queries (nothing new). More importantly, the total of average CTRs for top 10 queries amounts to 208%. This means that users click more than twice on the first result page. It is obvious that top 5 queries bring huge traffic to the website, but users often go deeper in Google results, and visibility of a website on further positions might also be profitable.

Average CTR of brand queries

My study concluded that if users search using brand queries, the position in SERP remains less important than on average, for all queries in the study. Although it is important, the total of average CTRs in top 10 queries amounts to 306%, which means that users click more than three times on the first result page. It could be the result of the fact that users don’t care about the positions for such phrases, or they are trying to find what they are looking for on various kind of sources: company websites, blogs, online stores, social profiles, and so on.

Average CTR of product queries

It is clear that when users search for products, the first result is most important for them (average CTR for top1 is 53%). The total of average CTRs is 208%, so we can affirm that users click on more than two results. This might result from comparing offers on different pages. If users don’t find products they search for on the first result page, they will keep looking further in SERPs. (More information about CTRs on further result pages have been presented in the end of this study.)

Average CTR of general queries

For general queries (non branded, and non product) the average CTR graph looks very natural.

Below you can find summarizing graph for all tested queries, including brand, product, and general queries CTRs.

As you can see on the graph, the average CTRs for all product and general queries are quite similar. Brand queries' average CTRs seems unnatural, but we can be sure that users care less about position in SERPs while using brand queries.

Long tail queries

Below you can see the average CTRs for long tail queries, containing 3, 4, and 5 words.

It is clear that when users make their queries more and more precise, the results are getting more accurate. For queries built with 4 and 5 words, visibility on the highest positions becomes increasingly important.

Let’s take a look on the average CTRs for positions 1 to 10, for long tail phrases built with 3, 4, and 5 words.

As it can be seen on the graph above, the more precise query, the more important it is to reach higher positions in SERPs. Long tail queries are very important because of the huge quality of traffic they generate. The surprising conclusion is that when users use longer queries, they open more websites from search engine result pages. The total of average CTRs in top 10 results for long tail queries (3 words) is 227%, for long tail queries (4 words) is 233%, and for long tail queries (5 words) it is 249%. We can only presume that this phenomenon occurs because search engine result pages meet the needs of users and it encourages the users to visit more than one website.

Additional data for further result pages

During my analysis, I also gathered the average CTR data for further result pages (positions 11-20, 21-30 and 31-40). We must remember that Google Webmaster Tools provide data for further positions, but denominator used for the CTR calculation is different for result page number 1, 2, 3, and so on. This occurs because it is based on the number of page views, not the number of searches. CTR on further result pages might be also distorted by the universal search, leading me to believe that data for further result pages might be less accurate than for the first one.

Below you can see the average CTRs for further result pages for the following queries:

Full methodology

In the first part of study, I built the database of queries, their CTRs, and their average positions. All data was gathered from Google Webmaster Tools from different types of websites.

I took the data from:

Clothes e-commerce websites

Drugstore e-commerce websites

Health and beauty e-commerce websites

Higher education institutions websites

Jewelry company websites

Websites providing song lyrics

Two classifieds websites on pets and animals

Websites with heavy machinery classifieds

The collected database includes various queries, which gives us some broader perspective. All the queries were collected from Polish websites, although I’m sure that the conclusions would prove right for all languages. The database includes 14507 queries. Having collected all the keywords, I rounded up the average positions.

Finally, each query was analyzed and marked in the appropriate category. The query was marked as a "Brand" if it contained the domain name, but was checked manually in case there were some entries to those websites from the incorrectly written domain names. It turned out that users made some mistakes quite often. For example, if the keyword was containing a small mistake, it was also marked as brand keyword. Queries were checked and marked as “Product” if they contained product names. If the query was neither Brand nor Product, it was marked as “General.”

After the analysis, I was left with:

14507 All queries

418 Brand queries

11684 Product queries

1795 General queries

3538 Long tail queries containing 3 words

1638 Long tail queries containing 4 words

809 Long tail queries containing 5 words

Final conclusions

Depending on the type of queries users choose, their actions in web search differ. We have to remember this fact while planning long-term SEO projects. While scheduling our long-term work, we can prioritize queries and plan to reach the highest positions first with the most important product queries, then with general queries, and after that with brand queries. Putting huge emphasis on website optimization so that it will serve good landing pages for those queries is key.

40 Comments

I found your one comment interesting:"The surprising conclusion is that when users use longer queries, they
open more websites from search engine result pages . . . We can only presume that this phenomenon occurs because search engine result pages meet the needs of users and it encourages the users to visit more than one website."

I drew the exact opposite conclusion from this. The longer the query, I'd think the less likely you are to find what you're looking for on the first try. I'd think the reason these searchers are opening so many pages is because they're NOT finding what they're looking for. The more specific your query is, the fewer existing websites there will be about it. Personally, when I put in long-tail queries--even in quotes--I usually find myself ending up on page two, still looking for a satisfying answer or result. I'd love to hear others' experiences with this.

Of course, really short queries aren't going to be satisfying either. One word usually isn't specific enough. It seems like 2-3 words is a happy medium. Of couse, I'm basing this on nothing but assumptions.

Thanks underrugswept for your very intresting comment. Like i said in the article "We can only presume..."

I think that such behavior depends on the exact query and sometimes my, and sometimes your opinion is true. If user uses properly constructed query, he will get good results and will open more sites. If the query won't give him good results, he might open more sites with belief, that on one of opened sites, there will be what he is looking for.

I also thing this depends on the type of query you are looking at. For example if you are doing research on a topic and using an informational query you might open up multiple websites to get accurate information versus a transactional query where the multiple windows are most likely because the original website did not bring them to the product they were looking for or such.

Hence a great follow-up would be these queries with associated landing pages.

This is great information basis to show anyone who wants to understand that users behave different depending on the type of queries.

Generally the cost and competition lowers with longer quires. Therefore I agree with you that anyone should have in mind to prioritize important long queries like product queries and not only lay focus on the broad ones.

One thing more to remember, different types of queries deserves different types of results.

Thanks for sharing this data Roman, really interesting stuff. It's certainly going to be useful for in in directing SEO efforts - as you can project the £/$/
€ benefits for increasing even a single position.

Its really feel good to know the variation of keywords while using GWT.

The way i optimize a website is that i categorize the keywords in four different forms like Long tail keywords, product keywords, category keywords and then brand keywords in last.Product based keywords are at first priority and category based keywords are at secondary to optimize. You probably missed to mention about category based keywords which also have lots of power to generate good sales.

Catalyst just released a new eBook that dives into Google CTR that looks at different search queries and the differences in average click through rates by top position, including search intent, query types, and user devices.

I would suggest giving it a read. You can download the white paper here.

I admire your effort Roman, it's always enjoyable to read an article written with such thorough analysis even though I cannot read it in one go :) but I promise I'll be back here to continue reading, great work bro!

great, product queries is more than rate in your table, but i think it's more regional key, 4 example in my Ukraine region, the general queries have more preference, product queries is middle preference

Amazing. I got some of the answers i was looking for regarding CTR. Your post is really helpful for me to atleast check the probability of traffic that one keyword can have. I know we can never estimate the exact amount but can now go somehow close to it.

Hi Roman, interesting research here. thanks for sharing. couple of questions for you: would it make sense to try to normalize your data so that the CTR's on the page always add up to 100%?

Also, can you describe how you treated ads in this study? obviously ads take up a ton of space (and clicks) on the SERP these days, particularly for product searches (Google Product Listing Ads) and branded searches. is this study only based on organic rankings? These CTR's just seem quite a lot higher than what i'm seeing in my own internal research. thanks for clarifying.

I actually thought it would be lower. I have not really had a Google search return the ideal result, the first time, for ages. In my opinion, the results displayed are not as useful as they once were. It doesn't matter if I am signed in or on a new browser, the results are always askew from what I wanted.

Nice article. I have a few clients where we have position one listings and the highest CTR were are getting is around 20% for a non-branded search. This maybe because it is a UK listing. Interested in others thoughts

Great post and analysis. Your analysis definitely reaffirms the need to concentrate on your short-tail traffic then work your way down to the longer tail.

Wouldn't it be cool if the data was correlated with the type of call-to-actions used in the meta-description. For e-commerce websites for example. It would be good to understand if CTR is driven by a specific call-to-action or is price led.

Excellent article, Roman! I think I've read almost every major Google CTR study over the past few years and this is the first one segmenting by search intent. The methodology was also clearly outlined.

Were all of your queries non-localized? or were there some geo-targeted as well (ex. warsaw jewelers)?

I love the effort Roman! I think it was already said but it would be interesting to include pricing metrics into your study. Especially for the e-commerce long tail keywords versus the general queries. I personally think this would really example the enormous cost difference between targeting the long tail versus the short tail as well as the difference in cost based on position.